In the original work on nested MCTS [1]}, a random policy was adopted for sampling. In this paper we will instead change it to the famous UCB policy [2]}. Given a local \(MAB_i\) , with the set of all the possible choices \(k_i\) , the UCB policy can be defined as:
\(UCB: \operatornamewithlimits{argmax}_{arm_j\in k_i} \bar{R}(k_i, arm_j) + \alpha \sqrt{\frac{2\ln n_i}{n_j}}\) 
